Binary RDF in Sesame

Since release 2.5.0, Sesame supports reading and writing a binary RDF serialization format. Its main features are reduced parsing overhead and minimal memory requirements (for handling really long literals, amongst other things).

Unfortunately, however, the format is currently not documented in the Sesame manuals. I have therefore done a bit of reverse-engineering and actually tried to reconstruct from the code how the format works. This post is a first draft of my reconstruction of the format.

Warning: dry technical stuff coming up. Read at your peril.

MIME Content Type

Sesame assigns the content type application/x-binary-rdf to its format.

Overall design

Results encoded in the Sesame Binary RDF format consist of a header followed by zero or more records, and closes with an END_OF_DATA marker (see below). Values are stored in network order (Big-Endian).

All string values use UTF-16 encoding. Reference ids are assigned to recurring values to avoid having to repeat long strings.

Header

The header is 8 bytes long:

Bytes 0-3 contain a magic number, namely the ASCII codes for the string
“BRDF”, which stands for Binary RDF.

Bytes 4-7 specify the format version (a 4-byte signed integer).

For example, a header for a result in format version 1 will look like this:

END_OF_DATA (byte value: 127):
This indicates the end of the data stream has been reached.

Strings

All strings are encoded as UTF-16 encoded byte arrays. A String is preceeded by a 4-byte signed integer that encodes the length of the string (specifically, it records the number of Unicode code units). For example, the string ‘foo’ will be encoded as follows:

URIs

URIs are recorded by the URI_VALUE marker followed by the URI encoded as a string.

Blank nodes

Blank nodes are recorded by the BNODE_VALUE marker followed by the id of the blank node encoded as a string.

Literals

Depending on the specific literal type (plain, language-tagged, datatyped), a literal is recorded by one of the markers PLAIN_LITERAL_VALUE, LANG_LITERAL_VALUE or DATATYPE_LITERAL_VALUE. This is followed by the lexical label of the literal as a string, optionally followed by either a language tag encoded as a string value or a datatype encoded as a string.

Value reference declaration records

To enable further compression of the byte stream, the Binary RDF format enables encoding of reference-identifiers for often-repeated RDF values. A value reference declaration starts with a VALUE_DECL record marker (1 byte, value 3), followed by a 4-byte signed integer that encodes the reference id. This is followed by the actual value, encoded as an RDF value (see above).

For example, a declaration that assigns id 42 to the URI ‘http://example.org/HHGTTG’ will look like this:

Explanation: byte 0 marks the record as a VALUE_DECL, bytes 1-4 encode the reference id, byte 5 encodes the value type (URI_VALUE), bytes 6-9 encode the length of the string value, bytes 10 and further encode the actual string value as an UTF-16 encoded byte array.

Note that the format allows the same reference id to be assigned more than once. When a second value declaration occurs, it effectively overwrites a previous declaration, reassigning the id to a new value for all following statements.

Namespace records

A namespace declaration is recorded by the NAMESPACE_DECL marker. Next follows the namespace prefix, as a string, then followed by the namespace URI, as a string.

For example, a namespace declaration record for prefix ‘ex’ and namespace uri ‘http://example.org/’ will look like this:

Statement records

Each statement record starts with a STATEMENT marker (1 byte, value 1). For the encoding of the statement’s subject, predicate, object and context, either the RDF value is encoded directly, or a previously assigned value reference (see section 2.3) is reused. A Value references is recorded with the VALUE_REF marker (1 byte, value 6), followed by the reference id as a 4-byte signed integer.

An example statement

Consider the following RDF statement:

<http://example.org/George> <http://example.org/name> "George" .

Assume that the subject and predicate previously been assigned reference ids,
(42 and 43 respecively). The object value has not been assigned a reference id.

Explanation: byte 0 marks the record as a STATEMENT. Byte 1 marks the subject of the statement as a VALUE_REF. Bytes 2-5 encode the reference id of the subject. Byte 6 marks the predicate of the statement as a VALUE_REF. Byte 7-10 encode the reference id of the predicate. Byte 11 marks the obect of the statement as a PLAIN_LITERAL value, bytes 12-15 encode the length of the lexical value of the literal, and bytes 16-26 encode the literal’s lexical value as a UTF-16 encoded byte array. Finally, byte 28 marks the context field of the statement as a NULL_VALUE.

Buffering and value reference handling

The binary RDF format enables declaration of value references for more compressed representation of often-repeated values.

A binary RDF producer may choose to introduce a reference for every RDF value. This is a simple approach, but it produces a suboptimal compression (because for values which occur only once, direct encoding of the value uses fewer bytes than introducing a reference for it).

Another approach is to introduce a buffered writing strategy: statements to be serialized are put on a queue with a certain capacity, and for each RDF value in these queued statements the number of occurrences in the queue is determined. As the queue is emptied and each statement is serialized, all values that occur more than once in the queue are assigned a reference id. This is, in fact, the strategy employed by the OpenRDF Rio implementation of this format.

It is also important to note that reference ids are not necessarily global over the entire document: ids are assigned on the basis of number of occurrences of a value in the current statement queue. If that number drops to zero, the reference id for that value can be ‘recycled’, that is, reassigned to another value. This ensures that we never run out of reference ids, even for very large datasets.

Conclusions

The record marking approach ensures that the format can be read and written in a completely streaming fashion, reducing memory overhead and making it very suitable for large datasets.

The next steps are to do actually do a bit of analysis on this format. For example, I’d be keen to compare this approach in more detail with the HDT format, currently a W3C submssion. Aduna’s motivation for implementing its own custom format had to do with optimizing performance for datasets with very long literal values, but I haven’t yet seen any comparitive figures on byte size and processing speeds.